PBC4cip: A new contrast pattern-based classifier for class imbalance problems

Octavio Loyola-González, Miguel Angel Medina-Pérez, José Fco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, Raúl Monroy, Milton García-Borroto

Research output: Contribution to journalArticle

34 Scopus citations

Abstract

© 2016 Contrast pattern-based classifiers are an important family of both understandable and accurate classifiers. Nevertheless, these classifiers do not achieve good performance on class imbalance problems. In this paper, we introduce a new contrast pattern-based classifier for class imbalance problems. Our proposal for solving the class imbalance problem combines the support of the patterns with the class imbalance level at the classification stage of the classifier. From our experimental results, using highly imbalanced databases, we can conclude that our proposed classifier significantly outperforms the current contrast pattern-based classifiers designed for class imbalance problems. Additionally, we show that our classifier significantly outperforms other state-of-the-art classifiers not directly based on contrast patterns, which are also designed to deal with class imbalance problems.
Original languageAmerican English
Pages (from-to)100-109
Number of pages89
JournalKnowledge-Based Systems
DOIs
StatePublished - 1 Jan 2017
Externally publishedYes

Fingerprint Dive into the research topics of 'PBC4cip: A new contrast pattern-based classifier for class imbalance problems'. Together they form a unique fingerprint.

  • Cite this

    Loyola-González, O., Medina-Pérez, M. A., Martínez-Trinidad, J. F., Carrasco-Ochoa, J. A., Monroy, R., & García-Borroto, M. (2017). PBC4cip: A new contrast pattern-based classifier for class imbalance problems. Knowledge-Based Systems, 100-109. https://doi.org/10.1016/j.knosys.2016.10.018